Artificial intelligence (AI) has to be trustworthy to succeed not only in real-world applications but also in safety-critical settings such as clinical diagnostics. From the engineering perspective (ignoring social and psychological factors), this trust is either established by showing exceptionally robust performance and succeeding under diverse conditions, or by providing transparent decision-making and explaining precisely when and why a model failed or prevailed [1]}, [2]}. While initially, robustness might sound like the more appealing option, it is difficult to achieve and verify [3]}, [4]}. This is of particular importance in medical applications where models are employed to support the diagnostic process of the physician, which, however, can neither adequately verify the model's predictions nor compare its decision process to their own. Explainable AI (XAI), on the other hand, allows for specific interactions with different stakeholders in a model's life-cycle such as, e.g., debugging and robustness for engineers, interaction and understanding for domain-experts, and fairness and data protection for regulators [5]}, [6]}, [2]}. In the case of regulators they are not only requesting explainability but moving in the direction of demanding it, and making concerns of social interest, such as ethics and safety, legally binding [8]}, [9]}, [10]}. Ultimately, both approaches are coexisting, but XAI should not be depreciated by arguing that models only have to be robust.
